The interaction between customers and businesses has increased exponentially with higher communication channel diversity across multiple touchpoints. McKinsey reports 94% of companies surveyed claim to positively experience the new omnichannel sales model in comparison to the sales model they used before. It is common knowledge that customer experiences (CX) determine a company’s ability to pull ahead of the competition. But for CX to become compelling and advantageous, it must incorporate connected experiences that foster lasting relationships and loyalty.

Connected CX consumes data generated from automated systems and intelligent technologies to deliver gratifying services adaptable to changing tastes and demands. Due to these favorable outcomes, connected CX satisfies customers by meeting their needs and expectations through personalized and curated offerings at any point. The cumulative effect of it translates into a customer’s effortless buying journey that enhances business conversions, loyalty, and bottom-line growth. Providing an effortless and friction-free customer journey has become so vital that lagging behind will prove costly for any business.

Do we know why organizations that offer connected CX have a higher capability to meet customers’ evolving needs and build long-term relationships?

Consistency Builds Trust:

A business must coordinate each customer touchpoint along the customer journey and align its efforts across all interaction channels. For instance, if a marketing strategy emphasizes a hassle-free return policy, but an unfamiliar in-store associate creates problems for the customer, the customer may not return. Consistency in customer service across a brand’s relationship with a customer is essential in ensuring satisfaction, building trust, and loyalty, thereby preventing customers from seeking other options.

Customers who receive exceptional service and positive experiences throughout their interaction with a brand will come to anticipate the same level of service and experiences in the future. Conversely, customers encountering poor service with a business will likely form negative assumptions that may lead to losing future business opportunities. Simply put, consistency in customer interactions translates into repeated purchases. Even a single negative experience can cause customers to move on to other brands. Providing customers with a consistent experience every time is the key to achieving long-term success.

Convenience Facilitates Switching Between Channels:

Offering multiple channels to interact with customers is the norm now as it fits perfectly with modern consumer behavior. Moreover, customers who engage with a business through various digital touchpoints are more likely to convert and become repeat customers. An omnichannel experience enables customers to interact with a company, enabling them to switch between multiple channels as part of a seamless customer journey.

Marketing, sales, customer support, and in-store experiences are coordinated, allowing customers to easily transition from one touchpoint to another to complete their purchase. For example, a customer browsing a social media channel finds an ad for a fashion retail store and discovers a pair of shoes they love. After clicking the ad, they land on the store’s social media handle, obtain more information about the shoes, and read customer reviews. Next, they navigate to the store’s website to place an order, but their size is out of stock.

They use the live chat widget to ask an agent when their size will be available again. The agent informs them that their size is currently in stock at the customer’s nearest store. The agent reserves the shoes for the customer, and the customer goes to the store the following day to make the purchase. The convenience of a connected CX can help companies build robust customer relationships, drive revenue growth, and improve operational efficiency.

Personalization Makes Customers Feel Valued:

Personalization is the cardinal element for brands nurturing customer relationships in today’s hyper-connected, competitive landscape. Companies rely on data integration across multiple channels and platforms to achieve personalization and create a holistic view of each customer. Businesses must collect, integrate, and analyze data from various sources, such as customer interactions, purchase history, and social media activity. As new channels and touchpoints emerge, they must adapt quickly and ensure that the CX remains tailored and personalized.

Customers who feel like a company comprehends and values their unique needs are likelier to become repeat customers. One way to provide a customized customer experience and reward loyal clients is by offering personalized recommendations and promotions based on their purchasing history. By analyzing customer data and employing machine learning algorithms, businesses offer suggestions that cater to the interests and needs of individual customers, making them feel that the brand puts effort into creating a tailored experience just for them.

The effect is a connection and trust between the customer and the business. By providing personalized recommendations, companies can create a more relevant and enjoyable experience for the customer, making customers feel valued and appreciated.

Efficiency Speeds up Engagement:

In the ever-evolving landscape of consumer behavior and habits, providing efficiency and speed while engaging the customers is critical to meeting expectations and satisfying and retaining customers. It’s essential to provide the efficiency of real-time engagement opportunities like live chats for convenience and immediate feedback. Brands that respond slowly risk losing customers who value efficient issue resolution. The fewer steps required to make a purchase, the less time customers spend, leading to higher satisfaction with the brand.

Failing to optimize efficiency in customer experiences can drive customers to competitors’ websites. When companies engage their customers with a proactive approach by offering complete assistance that addresses specific needs through proper communication and collaboration, it results in effective and personalized interactions. Efficient CX connects with customers to cater to their needs and even goes beyond to cater to their latent needs. Another focus area for efficient customer service offerings is securing customers from cybersecurity threats such as data breaches, identity theft, and ransomware attacks.

Companies must invest in robust security measures to mitigate these risks and ensure their data collection and storage practices comply with relevant regulations and compliance requirements.

Loyalty Leads to Increased Profitability:

Customer loyalty is a critical component of maximizing profitability for businesses. According to Forrest Research, acquiring new customers is five times more expensive than keeping existing ones. Therefore, a company that sells more products to repeat customers will ultimately have higher profits. It is essential to promote customer loyalty because gaining new customers at the expense of old ones is not a sustainable approach in competitive industries.

Measuring customer loyalty is straightforward and can be tracked by monitoring the number of customers who stop doing business with a company. The Gartner Group reported that 80% of future revenue would come from 20% of existing customers. Even when businesses launch new marketing campaigns, they are more likely to sell products to existing customers than new prospects. Building customer loyalty means prioritizing customer satisfaction over short-term sales numbers.

Selling a product that causes customer loss may result in record profits, but it will ultimately decrease the organization’s earning potential. Customer loyalty increases profitability by encouraging repeat business, reducing operating costs, establishing a reasonable price premium, and generating referrals. To summarize, promoting customer loyalty is essential for any business that wants to maximize profitability, and it is more cost-effective than acquiring new customers.

Connected CX Nurtures a Flawless Purchase Journey in Fulfilling Customer Delight:

As connected CX creates a consistent and engaging customer journey across all touchpoints, it eases the buying process navigation without disruptions or complications. This results in higher satisfaction rates, repeat purchases, and increased conversions. Customers on a smooth buying trail are likely to complete their purchases. Additionally, it saves costs as customers with effortless buying journeys are less likely to need assistance.

Customers with positive experiences remain loyal, and an effortless customer journey goes a long way in delivering customer delight. A connected CX-driven customer journey not only improves the user experience but also contributes to reduced support costs and increased efficiency for the brand. Companies that prioritize these elements will be able to provide a superior customer experience that sets them apart from their peers.

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Technology continues to catalyze transformations around the globe in every vertical. Given the momentum at which technology is evolving in these uncertain times, enterprises must plan ahead, keeping track of the trends and innovation. This applies to every technology function in businesses, including product information management.

Product information management (PIM) has advanced remarkably. It has come a long way since its early days when only large organizations could afford to invest in advanced analytics tools to boost productivity and manage product data and data syndication. According to recent research by MarketsandMarkets, the PIM market is expected to grow from $12.2B to $23.8B by 2027, with a compound annual growth rate of 14.3% during the forecast period.

The rising need to manage increasing volumes of product data owing to steady growth in e-Commerce and retail businesses has resulted in the growing adoption of advanced PIM systems. It’s fair to say that as technology continues to evolve, it will be critical for enterprises of all sizes to stay abreast with the latest PIM trends.

Here are the key PIM trends that can help product information management

• More Personalized Product Experience Management

Customers today have access to a pool of information and data that can help them make better purchasing decisions. And as they become more connected digitally, customer expectations for a personalized experience are rising—proactive service, personalized interactions, and connected experiences across digital channels.

Unlike a few years, how customers interact with products is changing, making personalization the center of customer experience. From tailoring the content customers see on a business website to providing customized product recommendations, the need for personalization has paved the way for product experience management (PXM).

Product experience management (PXM) may be a relatively new term in the business world, but it’s quickly gaining traction. PXM is the practice of using customer data to create personalized experiences for individual users. In the coming years, enterprises will delve more into PXM to provide customers with a personalized, dynamic, and targeted experience — enhancing the experience and better managing store variations of items and product descriptions.

In addition, enterprises will further enrich product information with automation, so their customers get an idea of the product, its use, and how it can benefit them. More enterprises will deploy automation across different channels and platforms that customers might use to find products, enhancing the personalization capacity.

The future PIM systems will be analytics-enabled, making it easy to centralize, manage, and distribute product information, enabling businesses to deliver compelling and satisfying product experiences.

• Intelligent PIM Will be More in Demand

Today’s consumers expect companies to provide them with product information quickly and update it in real-time. However, providing that information to customers can be challenging if the product information is locked within files, spreadsheets, and other disconnected repositories.

Cloud-based PIM systems are a great asset to businesses that seek to manage an extensive product portfolio while adhering to the latest retail and e-commerce trends. More companies will be adapting cloud-based PIM platforms — deploying intelligent solutions to make better decisions and strategies at all stages of creating and managing a product catalog. The need to migrate to intelligent cloud-based PIM will be in a rapid phase in the coming year.

Intelligent product information management is like a database

Intelligent product information management is like a database with an advanced interface that allows businesses to enrich simple descriptions with precise characteristics and images if a company connects its solution to a PIM or digital asset management (DAM) tool. Such solutions make it easier to decide what information is needed and where data is required. Then enterprises can distribute the structured data to all their eCommerce channels with the help of in-built algorithms.

At the same time, a spreadsheet could be a simple table of an unstructured or semi-structured data source. Leveraging cloud-based intelligent product information management solutions can empower businesses to improve customer experience, save time and effort, and gain better revenue. In the coming days, enterprises must deploy cloud and automation to optimize business, creating an intelligent product data ecosystem.

• Increased Demand for AI/ML in PIM Data Management

The PIM industry is wrapping its head around the idea that AI/ML will upend the legacy data management and distribution practices completely — this is a good thing. The technology has been available for decades but has only recently become affordable. Deploying AI/ML-powered PIM systems will not only allow enterprises to centralize large volumes of data but also helps improve the quality of information to make it publish-worthy across channels.

The advanced technology will be seen revolutionizing the legacy PIM paradigms by bringing in accuracy and reliability. Moreover, leveraging AI/ML will help businesses to track customers’ search patterns and journeys and collect the data. Companies will use the tech to analyze the data to understand how customers consume the product information from the different touchpoints, allowing them to improve product information for different target groups and channels.

Implementing AI/ML helps enterprises get a bigger picture of customer needs and behavior, optimizing online business visibility and various sales and marketing methods more effectively. Delivering high-quality product information to customers will remain a top priority for organizations. AI/ML-enabled PIM solutions will be crucial for enterprises to achieve better insights and a competitive edge.

The confluence of automation, AI/ML, and conversational commerce is shaping new possibilities for businesses and revolutionizing how they manage and enrich product information. These PIM trends can change how brands optimize product information and automate operations, omnichannel distribution, and brand experience.

• Next-level Data Visualization and Product Data Analytics

Data visualization is an integral element amid the efforts to become data-driven. This ability to turn raw data into clear, comprehensive insights is helping businesses exceed competition with tangible business outcomes. With the development of modern techniques for analyzing large data sets, data visualization and product data analytics have become increasingly crucial in PIM.

To make sense of the vast amount of available data, data visualization and product data analytics are essential tools for making informed decision-making. These tools are more than just charts and graphs; they are the ways to find hidden insights and the key to making better-informed decisions. Data visualization and product data analytics can help reduce costs and improve customer satisfaction and loyalty by digging for customer behavior insights and patterns.

In the years to come, enterprises will be more focused on leveraging these visualization and analytics tools to understand trends, identify opportunities, and make decisions. In a nutshell, by understanding how to use these tools, enterprises can gain insights into product data they never thought possible. A business can track product performance, understand customer behavior, and make better product development and marketing decisions.

• More Complementary to Product Data Management

The role of PIM in product data management (PDM) has been and will remain central to the business. Even though PIM and PDM are separate systems designed to manage product information, the tools and processes they use to manage product information are changing along with it. As companies are still figuring out how to make the best out of their data, they see much value in combining the two to improve their workflows and meet their business goals.

As PIM and PDM combine they will bring out new integrated systems and capabilities that will allow the two systems to be used together more efficiently while encouraging best practices that help productivity and efficiency. One major trend the PIM industry will see in the next year is a shift towards more PIM-PDM integration, fastening the product development process, leading to faster time-to-market and cost reduction.

Product information management will become more complementary to product data management, a standard capability that often is part of or incorporated into the product life cycle management (PLM) market.

• A More Comprehensive Range of Master Data Domains

eCommerce data is nuanced and complex, originating from many upstream and downstream systems and sources. However, the data amassed around products, suppliers, locations, and customers are connected. Understanding these connections unlocks incredible intelligence that can be leveraged in transformative ways.

When companies deploy a single-domain solution, they will likely sign up for added cost, resource strain, and fragmentation later. That’s because the need to scale and evolve is inevitable. In the coming years, enterprises will be implementing multi-domain approaches, giving businesses scalability with a long-term solution to manage their data and add additional domains as their needs change.

PIM will start supporting a more comprehensive range of master data domains and related use cases to expand the scope of PIM beyond the core use case of catalog management to include master data domains such as customer, vendor, and product.

Exploring and Keeping Up with the PIM Trends

Technology is rapidly evolving; by understanding the potential impact of relevant trends in PIM, businesses can create a roadmap to help achieve strategic goals. Whether an established player in the industry or a startup, enterprises can chart a course aligned with these trends to become more relevant.

In days to come, the future of PIM will be more rooted in technologies like advanced analytics, AI/ML, and big data automation, playing a significant role in predicting customer behavior and enhancing personalized experiences.

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A survey conducted by the National Retail Federation found that eCommerce product returns accounted for about 8 percent of all sales by respondents. With that figure amounting to about $260 billion in lost sales in the United States alone that year, any way of paring it down can bring in a substantial return of income (ROI).

Entreprises Look to Invest in Products and Platforms in the New Year.

Enterprises are looking to invest in products and platforms that can improve their bottom line, either through cost savings, increased profits, or both. Many companies, especially those that have been in the market for a long time, still struggle with decentralized, irregular means of managing product information.

Businesses are at a crossroads where data quality matters more than ever, and enterprises are going out of their way to keep customers supplied with accurate and relevant product data. Each piece of data can impact operational efficiency, customer experience, and ultimately ROI.

Standardize Product Data Across Channels

In many ways, standardizing product data across channels is the key to driving measurable ROI in eCommerce, which makes Product Information Management (PIM) inevitable. A PIM enables improved data accuracy, productivity, and customer reach.

Customers look for a frictionless path to buy products, and an organized product catalog through PIM implementation can amplify customer retention rates.

From centralizing product information to improving business productivity and powering content syndication to shortening the time to market — PIM’s positive impact on businesses are far-reaching.

Your Product Information Management not only enhances product data and product-related processes but also enables seamless syndication. These processes increase selling opportunities, as well as reduce returns and buyer frustration.

Benefits Directly Achieved Through PIM Implementation

1. Exceptional Productivity:

Streamlined processes and collaboration make the optimization of product data and digital assets a breeze. An advanced PIM solution can automatically update eCommerce platforms with product data from sources throughout the value chain, minimizing manual work and reducing the room for errors.

The process enables operational excellence, supply chain optimization, and order fulfillment accuracy, thus reducing instances of product returns, complaints, and even shopping cart abandonment.

In addition, advanced PIM streamlines product data management processes and facilitates collaboration across departments, resulting in more time and resources to focus on growing and scaling eCommerce operations — which all translate to higher online revenues and ROI.

PIM can empower enterprises with unlimited internal and external data sources that can be crafted accordingly to enable personalized online experiences for customers across channels. By centralizing product data and combining it with store analytics, PIM allows businesses to access insights that aren’t possible by comparing countless spreadsheets.

2. High Growth:

To thrive in an increasingly crowded and competitive eCommerce environment, enterprises need to differentiate themselves through engaging product experiences powered by accurate, complete, and consistent product data.

Customers, retailers, distributors, sales teams, and other stakeholders can have consistent product data anytime, anywhere, hence significantly reducing the chances of deviation from expectations or human error. PIM is a single repository to gather, manage, and expand product information and integrate it with other data sources and eCommerce channels.

Being a central hub for all product content, PIM helps businesses to increase product data quality, improve the team’s communication process, drive more sales, elevate customer experience, and significantly boost ROI.

Moreover, with robust and accurate product data and customer details, it is easier to target users with specific product needs, enabling cross-selling and up-selling, reducing search time, and increasing sales revenue.

An advanced PIM platform allows enterprises to gather, standardize, enrich, contextualize, and distribute product data. Plus, it can help accelerate time-to-market and speed up the enrichment process, giving businesses more selling days and slashing enrichment costs, all without increasing the number of errors in the product information.

In addition, completeness of product information facilitates informed purchases and eventually reduces returns.

3. Customer Loyalty:

“When customers connect with brands through multiple devices, portals, interfaces, websites, and apps, the accuracy and consistency of product data can significantly impact customer trust.”  When shopping online, accurate and up-to-date product information plays a vital role in converting potential buyers into actual buyers.

According to a report by Forbes, businesses that focus on improving customer experience achieve an 80% increase in their income. Data consistency and accuracy assure customers about the authenticity of product specifications, improve brand credibility, and build confidence.

By perfecting the product data, enabling increased upsell, and cross-sell opportunities, PIM helps businesses master the customer experience. In addition, an advanced PIM tool can enhance product pages, adding all the details necessary for a buyer to choose a product over competitors, boosting sales and revenue.

4. Scalability:

The number of products are subject to change as products are upgraded, modified, or changed over time. To manage and organize the purchasing, production, and communication across channels — relevant product data for multiple uses is critical.

PIM can provide complete data compilation transparency, so the latest changes stay updated when product information is updated, revived, and enriched by multiple teams across the value chain.

PIM can also act as a single point to gather and nurture product data and synchronously disseminate it across channels as the product line expands.

A repeatable, robust process to translate and localize content and push it out to appropriate markets or regions greatly increases a business’s ability to engage globally and be relevant locally. A research report by Facts & Factors reveals that the global cross-border B2C eCommerce market is forecasted to reach $4,820 billion by 2026, at a 27% CAGR through 2027.

PIM enables companies to easily and quickly onboard, validate, manage, localize, and publish the product information they need to market and sell across multiple distribution channels successfully. PIM can fast-track cross-border growth strategy that depends on expansion into new locales. This will enable businesses to achieve scalability across channels, resulting in reaching a wider user range and maximizing revenue.

5. Flexibility and Adaptability:

Quick turnaround times are crucial to eCommerce and digital success, enabling enterprises to plan and respond to changes in demand because of seasonal shifts, events, and new product launches. With a cloud-based PIM, businesses can configure the solution to take advantage of market opportunities easily.

The PIM platform offers flexibility to adapt and manage all data processes swiftly and seamlessly, resulting in multi-domain and multi-vector compatibility. With such capability, any size eCommerce business can gain an impactful omnichannel presence, ensuring that correct product data and information is accessible to customers at the right time.

In addition, it helps companies to expand their catalog and launch new products at the right time without leaving competitors the space to outshine. Also, accurate product information helps design compelling messages around the products.

With PIM’s complete, transparent, optimized, and reliable data, marketers can plan targeted promotions across sales channels for different geographic and demographic categories.

Achieve ROI with Automated PIM

The advancement in multi-channel marketing platforms provides ample opportunities for businesses to expand brand reach and increase ROI faster. However, investing in advanced PIM technology can drive more qualitative and quantitative ROI.

For enterprises looking to manage data of thousands of products and attributes, PIM’s capabilities support other systems and will produce the returns decision-makers seek. Instead of spending days in analytics programs calculating ROI, an automated PIM can enable enterprises to analyze and respond faster than competitors to generate more sales.

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Data-Informed Decisions are Needed for Digital Transformation

The needed understanding of data-informed decisions is because high-quality data is at the heart of all digitalization initiatives, from delivering invaluable insights to and uncovering latent operational efficiency strategies. And that’s the reason organizations’ must get careful about the creation of data silos.

Today  73.5% of most leading companies are data-driven in their decision-making. In almost every organization, data is collected from diverse sources to analyze and make business-critical decisions. And while these sources may number in the thousands and millions, having data silos building up across an organization is a natural outcome.

Despite modern-day databases and repositories being more robust, it is hard for them to completely ward off data silos, preventing them from realizing the true potential of their digital transformation initiatives.

The Data Silos Problems and Other Blocks to Digital Transformation

As a matter of fact, 89% of IT leaders today view data silos as one of the leading obstacles to digital transformation. The formation of silos are often a result of a combination of factors, including mergers and acquisitions, disconnected teams, interdepartmental dynamics, lack of data control, etc.

To prevent the formation of data pockets across organizations, enterprises must cultivate a data-sharing culture rather than a data-owning one. Eliminating silos begins with a cultural shift, necessitating a change in perspective that starts at the top in an organizational hierarchy. Enterprises can adopt several strategies to eliminate such data silos and prevent them from perpetuating.

Following Is a List of Ways to Keep Data Silos at Bay

Foster an Environment of Data Sharing

Various teams in a company hold data close to their chest, as data is knowledge, and knowledge is power. Various verticals typically work with exclusive jargon and processes pertaining to their own departmental goals. Each team sees itself as somewhat remote and distinct from the others, and segregated workspaces compound this spirit of disharmony.

All this leads to a sense of ownership and reluctance to share data with other teams among individual groups, which may harm the organization’s larger interests. Instead, organizations can nurture a culture of sharing and enabling a free flow of information. In doing so, they must also address the concerns of each group about data sharing and guarantee a mechanism to maintain data integrity.

Incentivizing and motivating individual teams to come together and nurture a culture of open data sharing and data unification is key to adopting enterprise-wide data connections. These initiatives resolve data silos, inspire a positive cultural change, turn the wheel of innovation, teamwork, and interdisciplinary efforts, and foster higher collaboration among the leadership.

Educate Departments About the Perils of Silos

Typically, various departments work in isolation, even while supporting one another to serve a common objective. Companies need to act as a single unit to optimize available datasets and improve team spirit, productivity, and output quality. While enterprise-wide sharing of information is key to augmenting productivity and generating novel opportunities, data silos pose a barrier to information accessibility, weakening the overall operational efficiency.

Operational inefficiencies can make discovering hidden opportunities difficult. Consequently, educating departments on how data silos endanger organizational success is critical to changing the overall data approach. It is essential to communicate to the teams about the benefits of collaboration and the adverse effects of silos. Promoting information sharing, transparency on task handling, and cross-functional cooperation breaks down silos.

Leaders must encourage team managers to prioritize addressing of silos and guide the whole organization to ensure a shift in perspective. The workforce needs to understand the basics of data silos and what can be done to fix them. They need to be aware of the data quality problems that stem from silos. To bridge the knowledge gap, enterprises must communicate the benefits of data sharing and data integrity, allowing the workforce to comprehend the shift better.

Evaluate the Causes Behind Silo Creation

If the challenge of data silo continues to persist, they start developing organically, once again reflecting on the organizational work culture. The enterprise setup itself enables silos to build up over time. It happens when every department gathers and amasses its data sets, each with its own guidelines, measures, and targets.

Teams working in various departments cultivate their style of getting things done or processing data in ways most suitable to their requirement. These practices cause silos to accumulate gradually.

The culture of working separately in various groups compounds the problem of silos. Besides this, the technology and data management systems often differ from department to department, spanning over tools such as spreadsheets, accounting software, or CRM. Besides, most legacy systems cannot handle information sharing as each solution stores and analyze data in distinct ways, which naturally paves the way for silos to grow over time.

Data needs continued care and systematic solution to manage and prevent the effortless accumulation of silos. Additionally, the best-of-breed technology acquired by enterprises could generate unintentional data silos too. Businesses that need specialized technology must keep an eye on this aspect.

Establish Cross-disciplinary Teams to Supervise

Companies across the globe are now centralizing data and sharing accurate versions of data to save time and cut costs. Enterprise-wide data glossary can be created to offer guidance across the board on data utility and storage. These data definitions equip interdisciplinary teams with pointers on how to comprehend data, create shared storage, and curb data silos.

Organizations need to upgrade their digital technology to keep up with the changing nature of data.

They need to sustain and evaluate data standards across the internal and external ecosystems. It is crucial to note that putting all data into a single system will not deliver the required result by default. Hence, it is essential for companies to create cross-functional teams to push the agenda of data integration forward.

Every step should be towards integrating data for the entire enterprise, including the various departments, to avoid recreating a new set of silos. It is crucial to integrate data discipline across all the departments and impart mindfulness about the innately dynamic nature of data.

Creating a Roadmap for Smooth Removal of Silos

With the advent of cloud technology, centralizing data for analysis is becoming easier and faster. Cloud-based tools rationalize the data gathering process into a shared pool, due to which tasks that once took months and years to complete now take days and hours.

The roadmap for data silos elimination needs to include finding a way to centralize data. A central data repository optimized for efficient analysis is the key to finding solutions for data silos. The next thing is to integrate data correctly and effectively to prevent future data silos.

Organizations can incorporate data using several methods such as scripting using writing scripts including SQL, Python, or other languages to transfer data from siloed data sources and into a data warehouse. On-premises extract, transform, and load (ETL) tools can also automate moving data from various sources to the data granary.

Cloud-based ETL is a sophisticated cloud-enabled process that is faster and easier. The process uses the cloud provider’s infrastructure, working competently in any environment. ETL tools provide ways to collect data from different sources into a centralized location for analysis and eliminates silos.

They also solve data integrity problems by ensuring new data is available to everyone. Data centralization consolidates data access and controls it with a data governance framework.

Connected Data Optimizes Digital Transformation

Data silos adversely impact productivity, insights, and collaboration. But they can stop being a source of trouble when data is centralized and optimized for processing and analysis. When an organization understands the value of having a single golden repository of data, it changes the culture inherently.

Digital transformation could not truly take place in an organization without first solving the problem of data silos. It takes multiple levels of effort to resolve this problem, including a change in culture, an assessment of short-and long-term tasks, the creation of cross-disciplinary groups, an understanding of data, and a plan to get it all working seamlessly.

While this might seem like a daunting task, going beyond gathering and evaluating data to solve the issue of data silos is instrumental to the success of any digital transformation journey. It begins when organizations migrate to a more proactive approach to leverage the value of connected data.

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By 2022, the total enterprise data volume is estimated to be more than 2.02 petabytes. As a result, businesses that work in highly data-intensive environments need robust data management capabilities to monitor, manage, store, access, secure, and share information in a streamlined and standardized way.

You’ll Want Your Business to Have the Best Practices in Data Governance

Consolidating and scaling more data sources and assets, an appropriate data governance architecture can help organizations maximize data value, minimize risks, and eliminate unnecessary operating costs.

A survey report by Gartner reveals that 55% of organizations lack a standardized approach to data governance and identify it as the most significant barrier to achieving data objectives. However, a carefully designed data governance strategy can solve many problems in terms of providing consistency, standardized data, and delivering better business outcomes.

Here are five key best practices for accomplishing data governance success:

• Define the proper process in alignment with the right people

Bridging the gap between the strategic teams and the data processes is critical in developing an inclusive and data-driven organization. To get a handle on data and control it from a centralized location, creating a unified database, along with a master dataset, is key.

This structure can standardize how data is used in various areas across the organization. Defined roles are essential to every data governance program, and it is vital to assign ownership levels. As such, the right people can access the best-suited data as they need it to provide the best insights – yielding the best results.

However, the data governance team should be cross-disciplinary, from data stewards to top-level executives. The team should comprise a cluster of subject matter experts, data security experts, project managers, and data governance visionaries who can deliver frontline and cross-functional experience to the entire organization.

• Build the roadmap that ensures trusted data always

Organizations looking to improve decision-making and business outcomes should align their business objectives with high-quality data creation and implementation. According to a Harvard Business Review report, 47% of data records are created with critical errors that affect work.

Companies often lack the processes for validating data characteristics, which include data accuracy, uniqueness, completeness, relevance, and timeliness. Moreover, organizations should build data quality controls to develop better insights and meet required data standards – tackling and identifying erroneous or inadequate data.

Organizations performing analytics without quality data can lead to inaccurate interpretations and decisions. Apart from data objectives, any additional goals that are unique to the business or necessary to address specific organizational goals should also be considered.

However, ensuring better and cleaner data should be paramount for businesses aiming at digital transformations and business analytics.

• Become consistently compliant with regulatory requirements

Adhering to critical compliance and regulatory mandates like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) is crucial for every data governance assessment. It is essential to ensure that organizations are consistently compliant with all levels of regulatory requirements to minimize risks and reduce operational costs.

Compliance ensures that data treatment follows applicable regulatory requirements, whether from the government, accreditation bodies, or the business itself. These regulations are designed to protect data from misuse, loss, and theft.

According to a survey conducted by erwin and UBM (erwin dotcom), 60% of organizations believe that regulatory compliance is the most essential factor in strengthening data governance. Regulations vary widely across geography and industry, complicating management tied to them.

However, organizations should follow best practices to ensure compliance and safeguard themselves from compliance failures that could lead to tarnished brand images and the subsequent downfall of the business.

• Evaluate risks across the board

The need to protect data and reduce risk is an essential factor in driving data governance at many organizations – data security and data privacy being the most visible ones. According to a survey by Gartner, 42% of data and analytics leaders do not assess, measure, or monitor their data and analytics governance.

Data security starts with understanding the risks related to data distributed across sources like data lakes, data warehouses, and individual silos. Also, it is important to protect the data across the organization to control data leaks, which mainly originate from improper data access permissions.

The organizational structure should ensure appropriate access to data while maintaining adequate privacy. Security threats that constantly evolve are unpredictable.

The best way to protect against data loss or theft is to stay updated on security risks and detect and respond to them on time. 

• Bring the right data management platform to the table

Regardless of the industry, unlocking the potential of data is only possible through proper data management. A data management platform is the backbone of any enterprise data strategy; choosing the right data management platform means selecting the firm’s long-term success.

A forward-thinking organization should look into the future, aligning its data governance expectations with its technology stack to implement robust quality controls, risk assessments, and ongoing monitoring and testing mechanisms. It can be done by opting for an Artificial Intelligence (AI)-driven, cloud-based platform that can deliver value, adapt to the data requirements, and evolve with the organizational changes.

A cloud-based data management platform will allow organizations to quickly plug into robust capabilities that are cost-efficient and will avoid the overhead required for on-premise servers. It can also radically simplify complex legacy operations, lowering running costs, improving agility, and gaining breakthrough performance that delivers real business value.

Safeguard the Future With Data Governance

Implementing advanced procedures and appropriate policies is essential for reliable data governance outcomes. Any business needs to unlock the value of data and boost reliable business decisions regardless of size and sector.

A recent study from McKinsey & Company reveals that companies, on average, are investing between 2.5% and 7.5% of their IT spend on data governance. This will enhance an organization’s strategic, operational, and tactical levels and deliver value, scale, and speed to the governance process.

In addition, to achieve futuristic analytics, visualizations, and automation goals, enterprises should strive to improve data quality (pimcore dotcom) and data access. 

Conclusion

Organizations should also realize that an integrated data governance architecture is essential to improve decision-making and ensure successful outcomes. Data should be well-documented and easily accessible; moreover, it should be secure, compliant, and confidential to manage risk and improve business decision-making.

Data governance is not a one-time strategy; it is an ongoing process that includes organizational tasks and responsibilities, regulatory requirements, and industry protocols. Data, one of any organization’s most significant assets, impacts decision-making and risk mitigation, and it should be governed accordingly.

Moreover, top-level executives and management should ensure organization-wide data awareness and quality improvement initiatives. Ultimately, they need to understand that data governance is unique to each organization, and data should be crafted accordingly to meet business demands.

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